Market phenomena - page 63
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Bevel is a direct correlation with thick tails. And according to the test, the maximum value of the bevel corresponds to the most probable value of stationarity of the residual from smoothing!!!!
This seems like a phenomenon to me. Or something I don't understand.
Uh-huh, you don't understand the fundamentals of the theorist.
The direct correlation with thick tails is not in the bevel, but in the kurtosis.
Uh-huh, you don't understand the fundamentals of the theorist.
The direct link to thick tails is not at the bevel, it's at the kurtosis.
This issue has been discussed on five. The excess does not have a direct link to the tails. If you're interested, look in my posts.
Very interesting.
Anonymous
Especially if there is justification in the theorist.
Posted somewhere, but left unattended. Regarding the bevel and thick tails.
Clockwise the inverse of the estimated dollar index.
We burn off the filter and get the remainder = the difference between the filter and the cotier
For the residual we change the lambda in HP and get the value of the slope and the probability that this residual is stationary (No fat tail???)
We see that the highest value of the bevel corresponds to the highest probability that the residue is stationary.
Very interesting opinion.
These concepts are not equivalent.
The residual can be a stationary value, and yet the tails of the distribution can be fat. It is easy enough to generate, say, a Laplaceian distributed quantity with independent "counts" and consider it a residual.
These concepts are not equivalent.
The residual can be a stationary value, and at the same time the tails of the distribution can be fat. It is easy enough to generate, say, a Laplace-like distributed quantity with independent "counts" and think of it as the residual.
I don't understand mathematical exercises very well.
Stationarity is variance = constant. Unattainable and it shows up in the test as the probability of stationarity not equal to 100%
The fat tail is the variability of the variance, which leads to an increase in the probability of events that are unlikely for a normal distribution.
But here is the s.c.o. graph.
Total triviality. Increases the smoothing power of the filter - increases the error
Incorrect. It is the constancy of the m.o. value and the dependence of the ACF only on the difference of the arguments. And this is the definition of stationarity - in a broad sense.
The fat tail is the variability of the variance, which leads to an increase in the probability of events that are unlikely to occur in a normal distribution.
Wrong. It is the constancy of the m.o. value and the dependence of the ACF only on the difference of the arguments.